Detection and Classification of Lung Diseases for Pneumonia and COVID-19 using Deep Learning Techniques - C. Hema Bharathi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Rapid advancements in medical imaging technologies have increased the need for sophisticated diagnostic instruments that can reliably and quickly identify lung diseases. The primary goal of this research is to develop and apply deep learning or machine learning methods for the diagnosis and classification of COVID-19 and pneumonia, two dangerous respiratory infections. The research investigates the potential of artificial intelligence in analyzing medical imaging data, such as X-rays and CT scans, using state-of-the-art computational models and algorithms to distinguish between healthy and diseased lung tissues. Using large datasets with a variety of COVID-19 and pneumonia case examples, machine learning or deep learning models are trained using the selected methodology. These respiratory disorders can be accurately identified and classified thanks to the models’ ability to learn complex patterns and features. The study also aims to explore the robustness and generalization capabilities of the models across different imaging modalities and populations. The results of the study offer a quick and non-invasive method for diagnosing lung conditions, which has important ramifications for medical diagnostics. The utilization of cutting-edge healthcare technologies has the potential to improve diagnostic precision, reduce staff workload, and enable timely interventions, especially when it comes to respiratory illnesses. The research’s conclusions and insights reinforce current initiatives to use artificial intelligence to enhance healthcare outcomes and solve issues with respiratory disease diagnosis and treatment.